Volume-6 , Issue-10 , Oct - 2018 Go Back

With the release of Bitcoin in 2008, the technology behind it, Blockchain which is based on Distributed Ledger Technology is getting popular day by day and there is continuous development and improvement in Distributed Ledger Technologies. In this paper we have not restricted ourselves to Blockchain and we have introduced some popular Distributed Ledger Technologies, a little bit about their technical implementation and areas where it can be used to make workflow easier, efficient and transparent. A number of publications and organizations websites and blogs have been analyzed to find the best practical applications of Distributed Ledger Technologies and various companies working on its implementation.

Distributed Ledger Technology, Bitcoin, IOTA, Nano, Radix, Directed Acyclic Graph, Hyperledger, nonce, Merkle Tree, Sharded Distributed Database

[1] S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” Www.Bitcoin.Org, p. 9, 2008.
[2] B. Version, “Ethereum: a Secure Decentralised Generalised Transaction Ledger,” 2018.
[3] L. C. Ltch, “Litecoin cash ltch,” 2018.
[4] Ripple, “Ripple Solution Overview,” Whitepaper, 2017.
[5] K. Thulasiraman and M. N. S. Swamy, “Graphs : theory and algorithms,” Neuroimage, vol. 2, no. 3, p. XV, 460 S, 1992.
[6] J. Weiner, “The Tangle,” New Yorker, vol. 81, no. 8, pp. 43–51, 2005.
[7] C. Lemahieu and C. Co, “Nano: A Feeless Distributed Cryptocurrency Network,” pp. 1–8, 2014.
[8] L. Baird, “Overview of Swirlds Hashgraph,” Swirlds, pp. 1–4, 2016.
[9] D. Hughes, “Radix - Tempo,” Radix DTL, no. September, pp. 1–15, 2017.
[10] M. Walport, “Distributed ledger technology: Beyond block chain,” Gov. Off. Sci., pp. 1–88, 2015.
[11] R. C. Merkle, “by Ralph C. Merkle Elxsi 2334 Lundy Place San Jose, CA 95131,” pp. 369–378.
[12] M. Jakobsoson and A. Juels, “Process of Work and Bread Pudding Protocols (Extended Abstract),” Pro- ceedings IFIP TC6/TC11 Jt. Work. Conf. Secur. Inf. Net- Work. Commun. Multimed. Secur., pp. 258–272, 1999.
[13] J. Siim, “Proof-of-Stake Research Seminar in Cryptography,” pp. 1–9, 2017.
[14] I. Bentov, C. Lee, A. Mizrahi, and M. Rosenfeld, “Proof of Activity: Extending Bitcoin’s Proof of Work via Proof of Stake,” Cryptol. ePrint Arch., vol. 452, no. 3, pp. 1–19, 2014.
[15] S. Dziembowski, S. Faust, V. Kolmogorov, and K. Pietrzak, “Proofs of Space,” pp. 585–605, 2015.
[16] J. Poon and T. Dryja, “The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments,” Tech. Rep., p. 59, 2016.
[17] L. Baird, “HASHGRAPH CONSENSUS: DETAILED EXAMPLES Hashgraph 2 Member 2 Event 2 Transaction 2 Consensus 2 Order 2 Timestamp 3 Gossip protocol 4 Self-parent 4 Other-parent 7 Graph 7 Hash 7 Hashgraph,” Swirlds Tech Rep., vol. 2, p. 29, 2016.
[18] A. Haeberlen, P. Kouznetsov, and P. Druschel, “The case for Byzantine fault detection,” Proc. 2nd Conf. Hot Top. Syst. Dependability-Volume 2, pp. 5–5, 2006.
[19] V. Buterin and V. Griffith, “Casper the Friendly Finality Gadget,” pp. 1–10, 2017.
[20] L. Baird, M. Harmon, and P. Madsen, “Hedera: A Governing Council & Public Hashgraph Network,” pp. 1–27, 2018.
[21] M. C. (Google), N. (Yahoo), P. P. (Yahoo), S. V. (Samsung R. America), and V. K. (Fairchild Semiconductor), “Applied Innovation Review,” Appl. Innov. Rev., no. 2, 2016.
[22] D. Nuernberg and A. Champion de Crespigny, “Blockchain in insurance: applications and pursuing a path to adoption,” Ernst Youngs Glob. Ltd., 2017.
[23] S. O’Hearn, J. Howe, S. Webb, G. Philipps, A. Wightmann, and M. Cook, “Blockchain: the $5 billion opportunity for reinsurers,” PwC Insur., p. 8, 2016.
[24] PricewaterhouseCoopers, “Blockchain - An opportunity for energy producers and consumers?,” PwC Glob. power Util., pp. 1–45, 2016.
[25] S. Lavanya, G. Lavanya, and J. Divyabharathi, “A Survey on Internet of Things for Healthcare and Medication Management,” vol. 2, no. 1, pp. 1–5, 2017.
[26] P. Brody, “How blockchain is revolutionizing supply chain management,” Ernst Young, 2017.
[27] M. O’Dair, Z. Beaven, D. Neilson, R. Osborne, and P. Pacifico, “Music On The Blockchain - Blockchain For Creative Industries Research Cluster Middlesex University,” no. July, p. 29, 2016.
[28] B. Anantharam and G. Guptha, “Security Issues in Various Cloud Computing : Solution and Occur-Rent Solutions,” vol. 1, no. 2, pp. 163–170, 2016

Faraz Masood, Arman Rasool Faridi, "An Overview of Distributed Ledger Technology and its Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.422-427, 2018.

Wireless Sensor Network (WSN) are the emerging and challenging technology with low processing and battery power. Security becomes a major issue in WSN; because of its wireless nature it is prone to various types of attacks and losing of data packet. Secure routing is important to avoid this type of issues. They are many techniques are available to provide secure routing to WSN. In the proposed work, our main aim is to find the trusted node and routing is done through the node to provide secure routing. The trusted node is identified by using Hidden Markov model (HMM) and it is rated. And also giving the un trusted node a chance to relay prove its identity. It provides the security features with minimum overhead and energy efficiency. The performance of proposed approach is illustrated that proposed model performs effectively compared with other existing approaches. Results demonstrated that developed HMM with trusted node provides significant performance in terms of memory overhead, delay, and Packet delivery ratio and energy consumption.

WSN, HMM, Trusted Node, Attacks, Routing, Intrusion Detection System

[1]. Chong, C. Y., & Kumar, S. P. (2003). Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247-1256.
[2]. Perrig, A., Stankovic, J., & Wagner, D. (2004). security in wireless sensor networks” communications of the ACM, vol. 47, no. 6.
[3]. Mourabit, Y. E., Toumanari, A., Bouirden, A., Zougagh, H., & Latif, R. (2014, November). Intrusion detection system in Wireless Sensor Network based on mobile agent. In Complex Systems (WCCS), 2014 Second World Conference on (pp. 248-251). IEEE.
[4]. Ning, P., Cui, Y., & Reeves, D. S. (2002, November). Constructing attack scenarios through correlation of intrusion alerts. In Proceedings of the 9th ACM Conference on Computer and Communications Security (pp. 245-254). ACM.
[5]. Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks.
[6]. Shanthi, S., & Rajan, E. G. (2016, October). Comprehensive analysis of security attacks and intrusion detection system in wireless sensor networks. In Next Generation Computing Technologies (NGCT), 2016 2nd International Conference on (pp. 426-431). IEEE.
[7]. 7. Panigrahi, R., Sharma, K., & Ghose, M. K. (2013). Wireless Sensor Networks–Architecture, Security Requirements, Security Threats and its Countermeasures. CS & IT-CSCP, AIRCC Publishing Corporation.
[8]. 8. Padmavathi, D. G., & Shanmugapriya, M. (2009). A survey of attacks, security mechanisms and challenges in wireless sensor networks. arXiv preprint arXiv:0909.0576.
[9]. 9. Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks.
[10]. 10 . Karl, H., & Willig, A. (2007). Protocols and architectures for wireless sensor networks. John Wiley & Sons.
[11]. 11. Wood, A. D., & Stankovic, J. A. (2002). Denial of service in sensor networks. computer, 35(10), 54-62.
[12]. 12. Zhang, Y., Liu, W., & Lou, W. (2005, March). Anonymous communications in mobile ad hoc networks. In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE (Vol. 3, pp. 1940-1951). IEEE.
[13]. 13. Boukerche, A., El-Khatib, K., Xu, L., & Korba, L. (2004, October). A novel solution for achieving anonymity in wireless ad hoc networks. In Proceedings of the 1st ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (pp. 30-38). ACM.
[14]. 14. Jiang, J. R., Sheu, J. P., Tu, C., & Wu, J. W. (2011). An Anonymous Path Routing (APR) Protocol for Wireless Sensor Networks. J. Inf. Sci. Eng., 27(2), 657-680.
[15]. 15. Chen, C. W., & Tsai, Y. R. (2011). Location privacy in unattended wireless sensor networks upon the requirement of data survivability. IEEE Journal on Selected Areas in Communications, 29(7), 1480-1490.
[16]. 16. Acharya, U., & Younis, M. (2010). Increasing base-station anonymity in wireless sensor networks. Ad Hoc Networks, 8(8), 791-809.
[17]. 17. Huang, D. (2006, November). On measuring anonymity for wireless mobile ad-hoc networks. In Local Computer Networks, Proceedings 2006 31st IEEE Conference on (pp. 779-786). IEEE.
[18]. 18. Mehta, K., Liu, D., & Wright, M. (2012). Protecting location privacy in sensor networks against a global eavesdropper. IEEE Transactions on Mobile Computing, 11(2), 320-336.
[19]. 19. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14), 2826-2841.
[20]. 20. Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198-226.
[21]. 21. Alsemairi, S., & Younis, M. (2015, December). Clustering-Based Mitigation of Anonymity Attacks in Wireless Sensor Networks. In Global Communications Conference (GLOBECOM), 2015 IEEE (pp. 1-7). IEEE.
[22]. 22. Panigrahi, R., Sharma, K., & Ghose, M. K. (2013). Wireless Sensor Networks–Architecture, Security Requirements, Security Threats and its Countermeasures. CS & IT-CSCP, AIRCC Publishing Corporation.
[23]. 23. Duhan, S., & Khandnor, P. (2016, March). Intrusion detection system in wireless sensor networks: A comprehensive review. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 2707-2713). IEEE.
[24]. 24. Krontiris, I., Dimitriou, T., Giannetsos, T., & Mpasoukos, M. (2007, July). Intrusion detection of sinkhole attacks in wireless sensor networks. In International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics (pp. 150-161). Springer Berlin Heidelberg.
[25]. 25. Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: a review. International Journal of Distributed Sensor Networks.
[26]. 26. Anagha S. Anand, V. S. Anitha Implementation and Analysis of k-Barrier Coverage in Wireless Sensor Networks. International Journal of Computer Sciences and Engineering Vol-6, Special Issue-6, July 2018.
[27]. 27. H. Kaur , K. Kaur Review Paper on Cryptography Algorithms Used in Wireless Sensor Networks. International Journal of Computer Sciences and Engineering Vol-6, Issue-8, Aug 2018.

S. Jawahar, J. Adamkani, "Intrusion Detection System (IDS) with trusted nodes for improving security in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.428-435, 2018.

Wireless sensor network (WSN) is a wireless network with large number of sensor nodes or it could be defined as a set of distributed devices which could be used to monitor sensible and natural conditions. These sensors can execute processing and sensing tasks, and additionally equipped for communicating with each other. Because of the extensive area of its applications it became one of the premier research topics recently. The main objective of wireless sensor network is regularly to gather sensing data from all sensors to particular sink nodes (base stations) and after that performing additionally operations at these sink nodes. Currently wireless sensor networks are available as a subject at advanced undergrad and graduate levels at some universities around the world. One of the challenges in WSNs is to devise and implement high security protocols with limited resources. This paper debated the security issues; challenges and protocols that are discussed and reviewed on the basis of attributes (confidentiality, availability, freshness, encryption methods, MAC authentication, key management, attacks protected and scalability) and tabulated WSNs security protocols, on the basis of (authors, protocols, advantages, disadvantages and applications) based on researches done by the researchers.

WSNs, Attacks in WSN, Security Protocols

[1] S. Misra, I. Woungang and S. C. Misra, “Guide toWireless Sensor Networks”, c Springer-Verlag London Limited, pp. 1, 2009
[2] J. Zheng and A. Jamalipour, “Wireless Sensor Networks A Networking Perspective”, A John Wiley & Sons, Inc., Publication, pp. xxiii-2, 2009
[3] C. S. Raghavendra, K. M. Sivalingam and T. Znati, “wireless sensor networks”, Kluwer academic publishers New York, Boston, Dordrecht, London, Moscow, pp. xiv, 2004
[4] M. U. Aftab, O. Ashraf, M. Irfan, M. Majid, A. Nisar and M. A. Habib, “A Review Study of Wireless Sensor Networks and Its Security”, Communications and Network, Vol. 7, No.4, pp.172-179, 2015
[5] Kahina Chelli, “Security Issues in Wireless Sensor Networks: Attacks and Countermeasures”, Proceedings of the World Congress on Engineering, London, U.K. (Vol. 1, pp. 1-3, 2015
[6] Murat Dener, “Security Analysis in Wireless Sensor Networks”, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, Vol.10, No.10, pp.303501, 2014
[7] David Martins and Herve Guyennet, “Wireless Sensor Network Attacks and Security Mechanisms: A Short Survey”, pp. 313-320, 2010
[8] C. Dhivya Devi and B.Santhi, “Study on Security Protocols in Wireless Sensor Networks”, International Journal of Engineering and Technology, Vol.5, No.5, pp.200-207, 2013
[9] M. Revathi and Dr. B. Amutha, “A Survey on Security Protocols in Wireless Sensor Network”, International Journal of Pure and Applied Mathematics, 2017
[10] R. Sharma, Y. Chaba and Y. Singh, “Analysis of Security Protocols in Wireless Sensor Network”, IJANA 2010
[11] Y. Bazband and K. Fahimi, “Performance Comparison Wireless Sensor Network Security Protocols: LLSP and TinySEC”, International Journal of Engineering Sciences and Research Technology, 2014
[12] A. S. Ahmed, “An Evaluation of Security Protocols on Wireless Sensor Network”, TKK –T Seminar of Internetworking, 2009
[13] L. E. Lighfoot, J. Ren and T. Li, “An Energy Efficient Link- Layer Security Protocol for Wireless Sensor Networks”, IEEE EIT 2007 proceedings, pp. 233-238, 2007
[14] J. Ren, T. Li and D. Aslam, “A Power Efficient Link-Layer Security Protocol LLSP for Wireless Sensor Networks”, Military Communications Conference, pp. 1002-1007, 2005
[15] C. Karlof, N. Sastry and D. Wagner, “TinySec: A Link Layer Security Architecture for Wireless Sensor Networks”, Baltimore, Maryland, USA, pp. 162-175, 2004
[16] L. Tobarra, D. Cazorla, F. Cuartero, G. Diaz and E. Cambronero, “Model Checking Wireless Sensor Network Security Protocols: TinySec + LEAP”, in wireless sensor and actor networks, Springer, Boston, MA, pp.95-106, 2007
[17] L. Krontiris, T. Dimitriou, H. Soroush and M. Salajegheh, “WSN Link-Layer Security Frameworks”, wireless sensor network security, Athens Information Technology, Greece, pp. 142, 2008
[18] A. Perrig, R. Szewczyk, J. D. Tygar, V.Wen and D. E. Culler, “SPINS: Security Protocols for Sensor Networks”, Kluwer Academic Publishers. Manufactured in Netherlands, Vol.8 No.5 pp.521-534, 2002
[19] K. M Pattani and P. J Chauhan, “SPIN Protocol for Wireless Sensor Network”, International Journal of Advance Research in Engineering, Science and Technology, 2015
[20] S. Zhu, S. Setia and S. Jajodia, “LEAP: Efficient Security Mechanisms for Large Scale Distributed Sensor Networks”, Proceedings of the 10th ACM conference on Computer and communications security, Washington D.C., USA, Vol. 2, No.4, pp.500-528, 2003
[21] D. Alsoufi, K. Elleithy, T. Abuzaghleh and A. Nassar, “Security in Wireless Sensor Networks-Improving the LEAP Protocol”, International Journal of Computer Science and Engineering Survey, 2012
[22] A. A. Andhale and Prof. B. N. Jagdale, “Light Weight Security Protocol for Wireless Sensor Networks (WSN)”, International Journal of Engineering Research and Technology, 2014
[23] K. Ren, W. Lou and Y. Zhang, “LEDS: Providing Location- Aware End-to-End Data Security in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 7, No.5, pp.585-598. 2008
[24] J. G. Ndia, “A Survey of Security Protocols for Wireless Sensor Networks”, International Journal of Applied Computer Science, 2017
[25] T. Park and K. G. Shin, “LiSP: A Lightweight Security Protocol for Wireless Sensor Networks”, ACM Transactions on Embedded Computing Systems, Vol. 3, No.3, pp.634-660, 2004
[26] A. Sharma, G. Tripathi, M. S. Khan and K. A. Kumar, “A Survey Paper on Security Protocols of Wireless Sensor Networks”, International Research Journal of Engineering and Technology, 2015
[27] M. Luk, G. Mezzour and A. Perrig, “MiniSec: A Secure Sensor Network Communication Architecture”, IPSN Cambridge, Massachusetts, IEEE USA, pp. 479-488). 2007
[28] E. Sharifi, M. Khandan and M. Shamsi, “MAC Protocols Security in Wireless Sensor Networks: A Survey”, International Journal of Computer and Information Technology, A survey 1, 2014

A.K. Nuristani, Jawahar Thakur, "Security Issues and Comparative Analysis of Security Protocols in Wireless Sensor Networks: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.436-444, 2018.

Open source programming of arduino with case study of ESP32 has been studied. Open source programming is suitable for both students as well as for beginners. The basic concept of Open System Software (OSS), particularly its need, application and suitability for IoT has been studied with suitable example. One of the popular OSS platform i.e. Arduino is considered as a case study. Open source programming of Arduino for the beginners in the field of embedded system as well as IoT is carried out with the sample microcontroller ESP32. The reasons for popularity of ESP32 microcontroller, particularly integration of radio frequency module on its silicon chip, are discussed. It includes concept of open source programming, downloading and installation of open source Integrated development Environment (IDE) software, understanding of IDE functions, coding and finally burning of hex code into the flash memory of microcontroller. It also includes some technical insights of programming process. The present work is suitable for beginners as well as for new students in the field of electronics and computer science.

Open source programming, Arduino, ESP32

[1] Anusha Bharati, Ritika Thakur, Kavita Mhatre,“Protection of Industrial and Residential areas by Wireless Gas Leakage Detector using IoT and WSN”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.62-67, 2017.
[2] K.M. Rudrappa, B.P. Divakar,” A Programmable System on Chip (PSOC) for Active Power Filter (APF) Based on Cortex M3”, International Journal of Scientific Research In Network Security And Communication, Volume-5, Issue-2, 2017.
[3] Bhojaraj Barhate, Dipashri Sisodiya, Rakesh Deore, “Application of Speech Recognition: For Programming Languages”, International Journal of Scientific Research in. Computer Science and Engineering, Vol.06, Issue.01, pp.6-8, 2018
[4] D.M. Dhamdhere, “Systems programming”, Edition 1(2011).
[5] Hemant Malviya, Sita Solanki,Survey of Quality “Models for Evaluating Quality in OSS (Open Source Software)”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.1, pp.15-20, 2016.
[6] Krupali Mistry, Avneet Saluja, “An Introduction to Open CV using Python with Ubuntu”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 1 ,Issue 2 ,2016.
[7] Kumari Seema Rani, “Open Source Software: A Prominent Requirement of Information Technology”, International Journal of Scientific Research In Network Security And Communication, Volume-6, Issue-2, 2018.
[8] Leo Louis, “Working principle of arduino and using it as a tool for study and research”, International Journal Of Control, Automation, Communication And System, Vol.1, No.2, 2016.
[9] Programming Aurdino Getting started with sketches- McGraw Hill, 2011.
[10] John D. Blischak, Emily R. Davenport, Greg Wilson, “A Quick Introduction to Version Control with Git and GitHub”, PLOS Computational Biology, 2016.
[11] Neil Kolban, Kolbans book on ESP32, 2017.

Pravin Bhadane, Aparna Lal, "Beginners Approach to the Open Source Programming: Case Study Arduino with ESP32," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.445-448, 2018.

The IoT incorporates the network of physical devices (things) that are connected to the global Internet for transferring and performing computation in lager application area. Routing protocols are designed for Low Power and Lossy network (RPL) to support communication in the constrained network with thousands of such power-scarce nodes in IoT. Consequently, due to the increases in millions and billions of connected devices in the constraint network(s) all over the world, security has become the aspect of most significance. Lightweight Intrusion Detection System (IDS) is required to examine the malicious activity and malicious node in the network of IoT, to filter out different attacks in a resource-constrained environment. In this paper, the lightweight trust based intrusion detection methods covering the different attacks of the wireless sensor network and IoT are reviewed and discussed. This article may give the direction to the beginners to identify appropriate methods and observations for their future research in the application domain of IoT.

IoT, RPL, security, attacks, Trust-based, intrusion detation

[1] H.D. Ma., “Internet of things: Objectives and scientific challenges”, Journal of Computer science and Technology, Vol. 26(6), pp.919–924, 2011.
[2] L. Patra, U.P. Rao, “Internet of Things - Architecture, applications, security and other major challenges”, 2016 3rd International Conference on Computing for Sustainable Global Development, New Delhi, pp. 1201-1206, 2016.
[3] T. Winter, P. Thubert, A. Brandt, J. Hui, R. Kelsey, P. Levis, K. Pister, R. Struik, J. Vasseur, and R. Alexander, “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks”, RFC 6550, Internet Engineering Task Force, 2012.
[4] L. Wallgren, S. Raza, T. Voigt, “Routing Attacks and Countermeasures in the RPL-based Internet of Things” International Journal of Distributed Sensor Networks, Vol. 9(8), p. 794326, 2013.
[5] B.A. Alabsi, M. Anbar, S. anickam, “A Comprehensive Review on Security Attacks in Dynamic Wireless Sensor Networks based on RPL protocol”, International Journal of Pure and Applied Mathematics, Vol.118, pp. 653-667, 2018.
[6] D. Shreenivas, S. Raza, T. Voigt, “Intrusion Detection in the RPL-connected 6LoWPAN Networks”, ACM International Workshop on IoT Privacy, Trust and Security, pp. 31 – 38, 2017.
[7] D. Airehrour, “A Trust-based Routing Framework for the Internet of Things”, PhD Diss, Auckland University of Technology, 2017.
[8] H.S. Kim, J. Ko, D.E. Culler, J. Paek, “Challenging the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL): A Survey”, IEEE Communications Suveys and Tutorials, Vol. 19, No. 4, pp. 2502 –2525, 2017.
[9] M. Kaur, A. Mohd Aslam, “Big Data Analytics on IOT: Challenges, Open Research Issues and Tools”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018.
[10] C. Bekara, “Security Issues and Challenges for the IoT-based Smart Grid”, International Workshop on Communicating Objects and Machine to Machine for Mission Critical Application, Elsevier, pp. 532 – 537, 2014.
[11] P. Manickam, V. Muthu Ganeshan, M. Girija, “Comprehensive Approach in Studying the Behaviour of Contiki RPL Protocol in Diverse Data Transmission Ranges”, International Journal of Scientific Research in Network Security and Communication, Vol. 6, Issue. 3, pp. 37-42, 2018.
[12] A. Mayzaud , R. Badonnel, I. Chrismen, “A Taxonomy of Attacks in RPL-based Internet of Things”, International Journal of Network Security, Vol.18, No. 3, pp. 459 – 473, 2016.
[13] R. Mehta, M.M. Parmar, “A Survey on Security Attacks and Countermeasures in RPL for Internet of Things”, International Journal of Advance Research in Science and Engineering, Vol. 7, No. 3, 2018.
[14] A. Mayzaud, R. Badonnel, I. Chrisment, “A Distributed Monitoring Strategy for Detecting Version Number Attacks in RPL-Based Networks”, IEEE Transactions on Network and Service Management, Vol. 14, No. 2, pp. 472-486, 2017.
[15] A. Dvir, T. Holczer, L. Buttyan, “VeRA - Version Number and Rank Authentication in RPL”, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems, Valencia, pp. 709-714, 2011.
[16] F. Ahmed, Y.B. Ko, “A Distributed and Cooperative Verification Mechanism to Defend against DODAGVersion Number Attack in RPL”, International joint conference on Pervasive and Embedded Computing and Communication systems, Vol. 1, pp. 55–62, 2016.
[17] M. Nikravan, A. Moaghar, M. Hosseinzadeh, “A Lightweight Defense Approach to Mitigate Version Number and Rank Attacks in Low-Power and Lossy Networks”, Wireless Personal Communications, Springer, pp. 1 –25, 2018.
[18] C.R. Perez-Toro, R.K. Panta, S. Bagchi, “RDAS: Reputation-Based Resilient Data Aggregation in Sensor Network”, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, pp. 1-9, 2010.
[19] N. Djedjig, D. Tandjaoui, F. Medjek, “Trust-based rpl for the Internet of things”, in 2015 IEEE Symposium on Computers and Communication (ISCC). IEEE, pp. 962–967, 2015.
[20] N. Djedjig, D. Tandjaoui, F. Medjek, I. Romdhani, “New trust metric for the rpl routing protocol”, in 2017 IEEE The 8th International Conference on Information and Communication Systems (ICICS). IEEE, 2017.
[21] Z.A. Khan, P. Herrmann, “A Trust Based Distributed Intrusion Detection Mechanism for Internet of Things”, 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, pp. 1169-1176, 2017.
[22] F. Nygaard , “Intrusion Detection System In IoT”, Master`s thesis, NTNU, 2017.
[23] D. Airehrour, J. Gutierrez, S.K. Ray, “A Trust-Aware RPL Routing Protocol to Detect Blackhole and Selective Forwarding Attacks”, Australian Journal of Telecommunications and the Digital Economy, vol. 5, April 2017.
[24] F. Medjek, D. Tandjaoui, I. Romdhani, N. Djedjig, “A Trust-Based Intrusion Detection System for Mobile RPL Based Networks”, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, pp. 735-742, 2017.
[25] J. Caminha, A. Perkusich, M. Perkusich, “A Smart Trust Management Method to Detect On-Off Attacks in the Internet of Things”, Security and Communication Networks, pp.10, 2018.
[26] A. Mayzaud, R. Badonnel, I. Chrisment, “Monitoring and Security for the RPL-based Internet of Things”, Doctoral dissertation, University de Lorraine, 2016.
[27] A. Aris, S. F. Oktug, S. Berna Ors Yalcin, “RPL version number attacks: In-depth study”, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Istanbul, pp. 776-779, 2016.

Hiral Patel, Hiren Patel, Bela Shrimali, "A Survey on Trust-based Intrusion Detection for Version Number Attack on RPL," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.449-454, 2018.

Irrespective of internal or external factors when a person feels excessive pressure it reflects in his facial expression, speech and physiological behaviour signals. Instead of traditional questioner method of stress evaluation, researchers now a day’s take various audiovisual and bio-signals, like heartbeat rate, muscle activity, blood pressure (BP) and skin conductivity. Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), Respiration (RSP) and Skin Conductivity (SC) are highly used bio-sensors for capturing bio signals. ECG signal gives heart-beat rate, inter-beat interval, and heart rate variability (HRV). EMG sensor when fit at upper trapezius gives the reading of muscle contraction which may be correlated with emotional state. SC sensors provide conductance and resistance of the skin which can also be used as a feature of importance. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, sub-band spectra, multi-scale entropy, etc. along with audiovisual feature, got research attention in the process to find the best stress-relevant features and to correlate them with stress level. This article makes a detailed discussion of effectiveness of various bio signals for stress level and emotional state detection.

Bio signals, cognitive computing, automated emotion detection

[1]. R. Dhatchayini and K. Mohamed Amanullah, “Brain Tumor Detection Using Clustering Method”, International Journal of Computer Sciences and Engineering, september 2018.
[2]. M.U Nagaral1 and T. H. Reddy, “Predicting the Characteristics of a Human from Facial Features by Using SURF”, International Journal of Computer Sciences and Engineering, September 2018.
[3]. R. Zimmermann, L.M.Crespo, J.Edelmann, O. Lambercy, M.C. Fluet, R. Riener, M.Wolf and R.Gassert, “Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study”, Journal of NeuroEngineering and Rehabilitation, vol: 10(4), 2013.
[4]. J.A. Healey and R.W. Picard, “Detecting Stress during Real-World Driving Tasks using Physiological Sensors”, IEEE Transactions on Intelligent Transportation Systems, 2005.
[5]. S. Sager, D. Borth, B. Elizalde, C. Schulze, B. Raj, and I.Lane, and A. Dengel, “AudioSentibank: Large-scale Semantic Ontology of Acoustic Concepts for Audio Content Analysis”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, July 2016.
[6]. F. Al-Shargie, M. Kiguchi,N. Badruddin, S.C. Dass,A.F. M.Hani and T. B. Tang, “Mental stress assessment using simultaneous measurement of EEG and fNIRS”, Biomedical Optics Express, October 2016 .
[7]. J. Chu, I.Moon, and M.Mun, “A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand”, IEEE Transactions on Biomedical Engineering, Nov ember 2006.
[8]. J. Kim and E. Andre, “Emotion Recognition Based on Physiological Changes in Music Listening”, IEEE Transactions On Pattern Analysis and Machine Intelligence, December 2008.
[9]. K.ConnWelch,U. Lahiri, Z.Warren and N. Sarkar, “An Approach to the Design of Socially Acceptable Robots for Children with Autism Spectrum Disorders”, INT J SOC ROBOT ,2010.
[10]. A. Dutta, U. Lahiri , A.Das, M.A. Nitsche and D.Guiraud, “Post-stroke balance rehabilitation under multi-levelelectro therapy: a conceptual review”, Frontiers in neuroscience , December 2014.
[11]. A. Panat and A.Patil, “Analysis of emotion disorders based on EEG signals of Human Brain”, International Journal of Computer Science, Engineering and Applications, August 2012.
[12]. R. Agarwal and J.Gotman, “Computer-Assisted Sleep Staging”, IEEE Transactions on Biomedical Engineering, December 2001.

Chandan Jyoti Kumar, "Stress And Bio Signals: A Review of State of Art Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.455-459, 2018.

In this paper Genetic and Fuzzy hybrid approach is used to recognize the face from the data set to improve the reorganization rate. The proposed method is implements in a four steps. Any sensor device is used to sense the face attributes and measured the attributes used for biometrics based identity. In second step any collected data from the sensor device will be pre-processed. Various types of noises in the attribute will be removed for increasing the accuracy for post processing. In the third step various features required for matching will be extracted. These features are identified from the inputted image. In fourth step these collected features values are stored into the database as training set. Later on any input image attribute values are matched with the stored dataset features. Whom the features will be matched will return success else will return failure.

Biometrics , Face Reorganization , Hybrid , Genetic

[1] Jindal, V and Ramaiya, M. “Fingerprint Recognition based on Minutiae Extraction Principle”, International Journal of Management, IT and Engneering, 2(9):Pp.254 -259. 2012.
[2] Garg, V. Kumar, H. and Sinha, H. “Speech Based Emotion Recognition Based on Hierarchical Decision Tree with SVM, BLG and SVR Classifiers”,Proc of the IEEE National Conference on Communication in 15-17Feb. New Delhi, India. Pp.1-5. 2013.
[3] Ayoub, S. K and Nori, R. S. “Biometric Detection and Steganography”, International Journal of Computer Networks and Communications Security”,1(5): Pp.180-188. 2013.
[4] Desai, N. Dhameliya, k and Desai, V. “Feature Extraction and Classification Techniques for Speech Recognition”, International Journal of EmergingTechnology and Advanced Engineering, 3(12): Pp.367- 371. 2013.
[5] Girgis, M. R. Mahmoud, T. M. and Hafeez, T. A. E.“An Approach to Image Extraction and Accurate Skin Detection from Web Pages”,World Academy of Science, Engineering and Technology, 1(3): Pp.367-375 . 2007.
[6] Babatunde, I. G. Charles, A. O and Officer, A. C.“ Fingerprint Matching using Neighborhood Distinctiveness”, International Journal of Computer Applications,66(21): Pp.1-8. 2013.
[7] Lumini, A and Nanni, L. “Advanced Methods for two-class pattern recognition problem Formulation for Minutiae-Based Fingerprint Verification”, The Journal of the Pattern Recognition Letters, Hauppauge,29(2): Pp.142-148. 2007.
[8] Smith, B. Gosine, R and Mann, G. “Fast Training of Multi class Support Vector Machine”s, Faculty of Science,University of Copenhagen.Canada,3(107):Pp.107 112.2015.
[9] Meng, Y and Tiddeman, B. “ Implementing the Scale Invariant Feature Transform (SIFT) Method”, St. Andrews, 13(20): Pp.402– 411. 2008.
[10]Bansal, N. and Singh, S. “ Fusion Speech and Face Biometrics Using Enhanced Version of Genetic Algorithm”, International Journal of Advanced Engineering Research and Science , 3(8): Pp.113-117. 2016.
[11]Kukula, E. P. Blomeke, C.R.Modi,S. K and Elliott, S .J.“Effect of Human –biometric sensor Interaction on Fingerprint Matching Performance, Image Quality, and Minutiae Count”,International Conference on Information Technology and Applications, West Lafayette ,34(4): Pp.270-277. 2009.
[12]Georgakis, C. Petridis, S. and Pantic, M. “Visual-onlydiscrimination between native and non native speech”, IEEE International Conference on Acoustics, Speech and Signal Processing,Italy 46(12): Pp.4828-4832. 2014.
[13]Gupta, H. and Wadhwa, D. S.“Speech Feature Extraction and Recognition Using Genetic Algorithm”, International Journal of Emerging Technology and Advanced Engineering, 4(1): Pp.363-369. 2014.
[14]Gour,B. Bandopadhyaya,T. K and Sharma, S.”Finger- print Feature Extraction using Mid-point Ridge Contour Method and Neural Network”,International Journal of Computer Science and Network Security,8(7): Pp.99-103. 2008.
[15]Hsieh, C. T and Humanoid, C. S. “Fingerprint recognition Based on Fuzzy Neural Network”, International Conference on Circuit Systems, Signal and Telecommunications,19 Jan. Gold Coast, Australia.Pp.85-90.2007.
[16]Sukhija,P.Behal, S. and Singh, P. 2016.Face Recognition System Using Genetic Algorithm, International Conference on Computational Modeling and Security,Bengaluru, India85(26): pp.410-417.

Balkrishan Jindal, Tarsem Singh, "Face Recognition Using Hybrid Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.460-464, 2018.

With the growth of Internet and computer knowledge, more and more persons connect socially. People communicate with each other and express their views on social media, which may form a complex network of association. Entities in the social networks create a “relation structure” through several connections which produces a huge amount of information. This “relation structure” is the group or community that we are interested in research. Community detection is very imperative to disclose the structure of social networks, dig to people`s views, analyze the information dissemination and grasp as well as control the public sentiment. In recent years, with community detection becoming an essential field of social networks analysis, a large number of the academic literature suggested several approaches to community detection. In this paper, we first describe the concepts of the social network, community, community detection and criterions of community quality. Then we classify the methods of community detection into the following categories. And at last, we summarize and discuss these methods as well as the potential future directions of community detection.

Social Network Analysis, Community Detection, Graph Data, Massive Datasets, Disjoint Community Detection

[1] J. Bruhn, “The sociology of community connections,” Springer Science+Business Media B.V., 2011.
[2] S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486(3-5), p. 75 – 174, 2010.
[3] A. Lancichinetti and S. Fortunato, “Community detection algorithms: A comparative analysis,” Phys. Rev. E, 2009.
[4] L. G. S. R. J. H. R. S. K. P. S. Harenberg, G. Bello and N. Samatova, “Community detection in large-scale networks: a survey and empirical evaluation,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 6(6), p. 426–439, 2014.
[5] S. L. B.W. Kernighan, “An efficient heuristic procedure for partitioning graphs,” Bell System Technical Journal, vol. 49(2), pp. 291–307, 1970.
[6] A. Pothen, “Graph partitioning algorithms with applications to scientific computing,” Technical report.
[7] B. Bollob´as, “Modern graph theory,” Graduate texts in mathematics, 1998.
[8] J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intel., vol. 22(8), p. 888–905, 2000.
[9] E. R. Barnes, “An algorithm for partitioning the nodes of a graph,” Technical Report RC 08690, 1981.
[10] W. E. Donath and A. J. Hoffman, “Lower bounds for the partitioning of graphs,” IBM J. Res. Dev., vol. 17(5), p. 420–425, 1973.
[11] A. Y. Ng and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” In Advances in Neural Information Processing Systems, vol. 14, p. 849–856, 2001.
[12] B. Nadler and M. Galun, “Fundamental limitations of spectral clustering methods,” Advances in Neural Information Processing Systems, vol. 19, 2007.
[13] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” In L. M. L. Cam and J. Neyman, editors, Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297, 1967.
[14] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” 1981.
[15] A. Hlaoui and S. Wang, “Median graph computation for graph clustering,” Soft Computing, vol. 10(1), pp. 47–53, 2006.
[16] R. T. T. Hastie and J. Friedman, “The elements of statistical learning,” Springer Series in Statistics, 2001.
[17] M. M. M. J. Rattigan and D. Jensen, “Graph clustering with network structure indices,” In Proceedings of the 24th International Conference on Machine Learning, ICML ’07, New York, NY, USA, ACM, pp. 783–790, 2007.
[18] D. M. W. J. R. Tyler and B. A. Huberman, “E-mail as a spectroscopy: Automated discovery of community structure within organizations,” In M. Huysman, E. Wenger, and V. Wulfs, editors, Proceedings of the First Iternational Conference on Communities and Technologies., 2003.
[19] M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical Review Edition, vol. 69, 2004.
[20] M. E. J. Newman, “Fast algorithm for detecting community structure in networks,” Phys. Rev. Edition, vol. 69:066133, 2004.
[21] B. Hughes, “Random walks and random environments: Random walks,” Number v. 1 in Oxford science publications, 1995.
[22] H. Zhou, “Distance, dissimilarity index, and network community structure,” Physical Review Edition, vol. 67(6):061901, 2003.
[23] R. Winkler, “An introduction to bayesian inference and decision,” International series in decision processes, 1972.
[24] M. E. J. Newman and E. A. Leicht, “Mixture models and exploratory analysis in networks,” Proceedings of the National Academy of Sciences, vol. 104(23), p. 9564–9569, 2007.
[25] R. A. U. N. Raghavan and S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Physics review Edition, vol. 76:036106, 2007.
[26] W. Rand, “Objective criteria for the evaluation of clustering methods,” Journal of the American Statistical Association, vol. 66(336):, pp. 846–850, 1971.
[27] A. D. J. Leskovec, K. J. Lang and M. W. Mahoney, “Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters,” Internet Mathematics, vol. 6(1), p. 29–123, 2009.

Kamal Sutaira, Kalpesh Wandra, C. K. Bhensdadia, "A Comprehensive Analysis of Dis-Joint Community Detection Algorithms for Massive Datasets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.465-469, 2018.

At present, the stage of Internet has changed the way that the people express their views and opinions in Social Media. Millions of people are using social network sites like Facebook, Twitter, Google Plus, etc. to express their emotions, opinion and share views about their daily lives. Through the online communities, sellers get an interactive media where consumers inform and influence others by online forums. Social media is generating a large volume of sentiment rich data especially in the form of tweets, status updates, blog posts, comments, reviews, etc. Sentiment analysis has become a very popular field of research and lot of researchers have explored this field but still, there are many issues as sentiment analysis processes text-based unstructured data. The dictionary-based approach takes less processing time than supervised learning approach but accuracy is not up to the mark. Supervised learning approach provides better accuracy and it is found that sentiment classifiers are severely dependent on domains or topics. In this research paper, applications of sentiment analysis, types of approaches present, and evaluation metrics’ involved in sentiment analysis are discussed briefly.

Data Mining, Sentiment Analysis (SA), Emotion Detection (ED), Opinion Mining, Social Media Network, Applications, Evaluation Metrics

[1]. AkriviKrouska, Christos Troussas, Maria Virvou, “Comparative Evaluation of Algorithms for Sentiment Analysis over Social Networking Services”, Journal of Universal Computer Science, Volume 23, Issue 8, 2017, Pages 755-768.
[2]. Amreen Shaikh, Madhuri Rao, “Survey on Sentiment Analysis”, International Conference on Emanations in Modern Technology and Engineering, Volume 5, Issue 3, 2017, Pages 164 – 167.
[3]. Ankita Gupta, JyotikaPruthi, Neha Sahi, "Sentiment Analysis of Tweets using Machine Learning Approach", International Journal of Computer Science and Mobile Computing, Volume 6, Issue 4, 2017, Pages 444 – 458.
[4]. Anu Sharma and Savleen Kaur, “Techniques for Sentiment Analysis survey”, I J C T A, Volume 41, Issue 9, 2016, Pages 355 - 360.
[5]. Bing Liu. “Sentiment Analysis and Opinion Mining”, Morgan & Claypool Publishers, May 2012.
[6]. Brian Keith, Exequiel Fuentes, and Claudio Meneses. A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews. In Proceedings of ACM SIGKDD Conference, Halifax, Nova Scotia, Canada, 2017, Pages 1 – 10.
[7]. Edison M., A. Aloysius, “Concepts and Methods of Sentiment Analysis on Big Data”, International Journal of Innovative Research in Science, Engineering and Technology, Volume 5, Issue 9, 2016, Pages 16288 - 16296.
[8]. Edison M., A. Flora, D. Vinnarasi, A. Aloysius, “Sentiment Analysis: A Review”, International Journal of Engineering Technology and Computer Research (IJETCR), Volume 5, Issue 4, 2017, Page 26 - 30.
[9]. Govindarajan M., Romina M., “A Survey of Classification Methods and Applications for Sentiment Analysis”, International Journal of Engineering and Science (IJES), Volume 2, Issue 12, 2013, Pages 11-15.
[10]. Jianbo Yuan, Quanzeng You and JieboLuo, “Sentiment Analysis Using Social Multimedia”, Springer International Publishing Switzerland, Multimedia Data Mining and Analytics, 2015, Pages 31 - 59.
[11]. Mahima Gaur and JyotikaPruthi, “A Survey on Sentiment Analysis and Opinion mining”, International Journal of Current Engineering and Technology, Volume 7, Issue 2, 2017, Pages 444 - 446.
[12]. MrunaliOmprakashThakare, MrudulaNimbarte, “User Tracking Using Tweet Segmentation And Word Recognition”, International Conference on Electronics, Communication and Aerospace Technology, 2017, Pages 664 - 668.
[13]. Praveen Kumar, Umesh Chandra Jaiswal, “A Comparative Study on Sentiment Analysis and Opinion Mining”, International Journal of Engineering and Technology (IJET), Volume 8, Issue 2, 2016, Pages 938 - 943.
[14]. Purti Jain, ShrutiKohli, “Big Data Analysis, Algorithms, and Applications: A Survey”, International Journal of Emerging Trends in Engineering Research (IJETER), Volume 3, Issue 1, 2015, Pages 19 – 24.
[15]. Revathi S., N Rajkumar, “A Survey on Sentiment Analysis for Web-Based Data by Using Various Machine Learning Techniques”, International Journal of Engineering Trends and Applications (IJETA) – Volume 1, Issue 2, 2014, Pages 1 - 5.
[16]. Shailesh Kumar Yadhav, “Sentiment Analysis and Classification: A Survey", International Journal of Advanced Research in Computer Science and Management Studies, Volume 3, Issue 3, 2015, Pages 113 - 121.
[17]. Tapan Kumar Hazra, YashikaBihani, Sneha Mishra, “Sentiment Learning Using Twitter Ideograms”, IEEE, 2017, Pages 115 - 120.
[18]. Vidisha M. Pradhan, Jay Vala, PremBalani, “A Survey on Sentiment Analysis Algorithms for Opinion Mining”, International Journal of Computer Applications (0975 – 8887) Volume 133, Issue 9, 2016, Pages 7 - 11.
[19]. Vishal A. Kharde, S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications, Volume 139, Issue 11, 2016, Pages 5 - 15.
[20]. Walaa Medhat, Ahmed Hassan, HodaKorashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, Volume 5, 2014, Pages 1093 – 1113.
[21]. Yakshi Sharma, VeenuMangat, Mandeep Kaur, “Sentiment Analysis & Opinion Mining”, International Journal of Soft Computing and Artificial Intelligence, Volume 3, Issue 1, 2015, Pages 59 - 62.

P. Uma, A. Aloysius, "A STUDY ON APPLICATIONS AND CONCEPTS OF SENTIMENT ANALYSIS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.470-474, 2018.

The development of Internet of Things (IOT) technology became one of the proponents in the industrial revolution 4.0. Digital transformation began to be applied to the entire manufacturing industry, services, transportation and education which have slowly shifted utilizing IOT technology. The industrial revolution 4.0 has an impact on digital transformation and becomes a necessity that can change business patterns such as the ease of data interaction services between industries to customers that are also supported by ease of access and speed of decision making. However, in its development, stakeholders tend to focus on infrastructure and information systems, while the security of information systems is still a comfort zone for industries in the transformation to industry 4.0. The issue of information system security will be a challenge for the industry with open access to information systems; otherwise focus will hamper the business process of the industry. In this research will be discussed about the modeling and implementation of information system security with a combination of web-based security methods with port knocking firewall model and short message service gateway as a security medium with the concept of ease of access with safe and comfortable. The result of this research has been testing penetration testing using network tools.

Industry 4.0, cyber security, port knocking, short message service gateway

[1] Yampolskiy, R. V. 2014, Utility Function Security in Artificially Intelegent Agent. Journal of Experimental & Theoretical Artificial Intelligence. Sep2014, Vol. 26 Issue 3, p373-389. 17p. DOI: 10.1080/0952813X.2014.895114.
[2] Ratna, S. R., Ravi, R., &Shekhar, B., 2015, An Intelligent Approach Based on Neuro Fuzzy Detachment Scheme for Preventing, Jamming Attack in Wireless Networks. Journal of Intelligent & Fuzzy Systems. 2015, Vol. 28 Issue 2, p809-820. 12p. DOI: 10.3233/IFS-141363.
[3] Bouzar, B. L., Bouabana, T. T., &Benferhat, S., 2015, Instantiated first order Qualitative Choice Logic for an efficient handling of alerts correlation. Intelligent Data Analysis. 2015, Vol. 19 Issue 1, p3-27. 25p. DOI: 10.3233/IDA-140693.
[4] Blazek, P., Kuca, K., Jun, D., &Krejcar, O., 2017, Development of Information and management System for laboratory based on Open Source Licensed Software with Security Logs Extension. Journal of Intelligent & Fuzzy Systems. 2017, Vol. 32 Issue 2, p1497-1508. 12p. DOI: 10.3233/JIFS-169145.
[5] Sobeslav, V., Balik, L., Hornig, O., Horalek, Josef.,&Krejcar, O., 2017, Endpoint Firewall for Local Hardening in Academic esearch Environment. 2017. Journal of Intelligent & Fuzzy Systems, Vol. 32 Issue 2, p1475-1484. 10p. DOI: 10.3233/JIFS-169143.
Computer Network
[6] Stallings, W., & Brown, L., Computer Security Principles and Practice, Second. 2012
[7] Anderson, C., Baskerville, R. L., Kaul, M., 2017, Information Security Control Theory: Achieving a Sustainable Reconciliation Between Sharing and Protecting the Privacy of Information. Journal of Management Information Systems. 2017, Vol. 34 Issue 4, p1082-1112. 31p. 4 Diagrams, 2 Charts, 1 Graph. DOI: 10.1080/07421222.2017.1394063.
[8] de Souza, C.S.P., da Silva, S., & Jose, A., 2017, Security Management Benefit at Work in Monitoring Individual Protection Equipment (IPE) and Collective Security Systems (CSS), Procedures and Methodes in Industry Construction. Business Management Dynamics. Jan2017, Vol. 6 Issue 7, p19-26. 8p.
[9] Sobeslav, V., Balik, L., Hornig, O., Horalek, J., &Krejcar, O., 2017. Endpoint Firewall for Local Security Hardening in Academic Research Environment . Journal of Intelligent & Fuzzy Systems. 2017, Vol. 32 Issue 2, p1475-1484. 10p. DOI: 10.3233/JIFS-1691
[10] N. Yrvina. 2017. Cyber security Tips to Keep Your Firm Safe. Journal of Financial Planning. Jan2017, Vol. 30
[11] Herranz, J., & Nin, J., 2014, Secure and Efficient AnonymizationOf Distributed Confidential Databases. International Journal of Information Security. Nov2014, Vol. 13 Issue 6, p497-512. 16p. DOI: 10.1007/s10207-014-0237-x.
[12] Vavilis, S., &Petkovic, M., Zannone, N., 2016, A Severity –Based Quantification Of Data Leakages in Database Systems. Journal of Computer Security. 2016, Vol. 24 Issue 3, p321-345. 25p. DOI: 10.3233/JCS-160543.
[13] Hadavi, M., Jalili, Rasool.,Damiani, E., &Cimato, S., 2015, Security and Searchability in Scret Sharing-Based Data Outsourcing. International Journal of Information Security. Nov2015, Vol. 14 Issue 6, p513-529. 17p. DOI: 10.1007/s 10207-015- 0277-x.
[14] Chaniotis, I., Kyriakou, K.I., & Tselikas, N., 2015, is Node. Js aVisible Option for Building Modern Web Applications? A Performance Evaluation Study. Computing. Oct2015, Vol. 97 Issue 10, p1023-1044. 22p. DOI: 10.1007/s00607-014-0394-9.

Ashok Koujalagi, "Network Security Intelligence for Small and Medium Scale Industry 4.0: Design and Implementation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.475-485, 2018.